itk::AmoebaOptimizer Class Reference

#include <itkAmoebaOptimizer.h>

Inheritance diagram for itk::AmoebaOptimizer:

Collaboration diagram for itk::AmoebaOptimizer:

Public Types
using	ConstPointer = SmartPointer< const Self >
using	InternalParametersType = vnl_vector< double >
using	NumberOfIterationsType = unsigned int
using	ParametersType = Superclass::ParametersType
using	Pointer = SmartPointer< Self >
using	Self = AmoebaOptimizer
using	Superclass = SingleValuedNonLinearVnlOptimizer
Public Types inherited from itk::SingleValuedNonLinearVnlOptimizer
using	CommandType = ReceptorMemberCommand< Self >
using	ConstPointer = SmartPointer< const Self >
using	Pointer = SmartPointer< Self >
using	Self = SingleValuedNonLinearVnlOptimizer
using	Superclass = SingleValuedNonLinearOptimizer
Public Types inherited from itk::SingleValuedNonLinearOptimizer
using	ConstPointer = SmartPointer< const Self >
using	CostFunctionPointer = CostFunctionType::Pointer
using	CostFunctionType = SingleValuedCostFunction
using	DerivativeType = CostFunctionType::DerivativeType
using	MeasureType = CostFunctionType::MeasureType
using	ParametersType = Superclass::ParametersType
using	Pointer = SmartPointer< Self >
using	Self = SingleValuedNonLinearOptimizer
using	Superclass = NonLinearOptimizer
Public Types inherited from itk::NonLinearOptimizer
using	ConstPointer = SmartPointer< const Self >
using	ParametersType = Superclass::ParametersType
using	Pointer = SmartPointer< Self >
using	ScalesType = Superclass::ScalesType
using	Self = NonLinearOptimizer
using	Superclass = Optimizer
Public Types inherited from itk::Optimizer
using	ConstPointer = SmartPointer< const Self >
using	ParametersType = OptimizerParameters< double >
using	Pointer = SmartPointer< Self >
using	ScalesType = Array< double >
using	Self = Optimizer
using	Superclass = Object
Public Types inherited from itk::Object
using	ConstPointer = SmartPointer< const Self >
using	Pointer = SmartPointer< Self >
using	Self = Object
using	Superclass = LightObject
Public Types inherited from itk::LightObject
using	ConstPointer = SmartPointer< const Self >
using	Pointer = SmartPointer< Self >
using	Self = LightObject

Public Member Functions
virtual ::itk::LightObject::Pointer	CreateAnother () const
virtual const char *	GetNameOfClass () const
void	SetCostFunction (SingleValuedCostFunction *costFunction) override
void	StartOptimization () override
Public Member Functions inherited from itk::SingleValuedNonLinearVnlOptimizer
virtual const bool &	GetMaximize () const
virtual void	SetMaximize (bool _arg)
virtual void	MaximizeOn ()
virtual void	MaximizeOff ()
bool	GetMinimize () const
void	SetMinimize (bool v)
void	MinimizeOn ()
void	MinimizeOff ()
virtual const MeasureType &	GetCachedValue () const
virtual const DerivativeType &	GetCachedDerivative () const
virtual const ParametersType &	GetCachedCurrentPosition () const
Public Member Functions inherited from itk::SingleValuedNonLinearOptimizer
virtual ::itk::LightObject::Pointer	CreateAnother () const
virtual const CostFunctionType *	GetCostFunction () const
virtual CostFunctionType *	GetModifiableCostFunction ()
MeasureType	GetValue (const ParametersType &parameters) const
virtual void	SetCostFunction (CostFunctionType *costFunction)
Public Member Functions inherited from itk::Optimizer
virtual const ParametersType &	GetInitialPosition () const
virtual void	SetInitialPosition (const ParametersType &param)
void	SetScales (const ScalesType &scales)
virtual const ScalesType &	GetScales () const
virtual const ScalesType &	GetInverseScales () const
virtual const ParametersType &	GetCurrentPosition () const
Public Member Functions inherited from itk::Object
unsigned long	AddObserver (const EventObject &event, Command *)
unsigned long	AddObserver (const EventObject &event, Command *) const
unsigned long	AddObserver (const EventObject &event, std::function< void(const EventObject &)> function) const
virtual void	DebugOff () const
virtual void	DebugOn () const
Command *	GetCommand (unsigned long tag)
bool	GetDebug () const
MetaDataDictionary &	GetMetaDataDictionary ()
const MetaDataDictionary &	GetMetaDataDictionary () const
virtual ModifiedTimeType	GetMTime () const
virtual const TimeStamp &	GetTimeStamp () const
bool	HasObserver (const EventObject &event) const
void	InvokeEvent (const EventObject &)
void	InvokeEvent (const EventObject &) const
virtual void	Modified () const
void	Register () const override
void	RemoveAllObservers ()
void	RemoveObserver (unsigned long tag)
void	SetDebug (bool debugFlag) const
void	SetReferenceCount (int) override
void	UnRegister () const noexcept override
void	SetMetaDataDictionary (const MetaDataDictionary &rhs)
void	SetMetaDataDictionary (MetaDataDictionary &&rrhs)
virtual void	SetObjectName (std::string _arg)
virtual const std::string &	GetObjectName () const
Public Member Functions inherited from itk::LightObject
Pointer	Clone () const
virtual void	Delete ()
virtual int	GetReferenceCount () const
void	Print (std::ostream &os, Indent indent=0) const

Static Public Member Functions
static Pointer	New ()
Static Public Member Functions inherited from itk::SingleValuedNonLinearOptimizer
static Pointer	New ()
Static Public Member Functions inherited from itk::NonLinearOptimizer
static Pointer	New ()
Static Public Member Functions inherited from itk::Optimizer
static Pointer	New ()
Static Public Member Functions inherited from itk::Object
static bool	GetGlobalWarningDisplay ()
static void	GlobalWarningDisplayOff ()
static void	GlobalWarningDisplayOn ()
static Pointer	New ()
static void	SetGlobalWarningDisplay (bool val)
Static Public Member Functions inherited from itk::LightObject
static void	BreakOnError ()
static Pointer	New ()
using	CostFunctionAdaptorType = Superclass::CostFunctionAdaptorType
NumberOfIterationsType	m_MaximumNumberOfIterations
ParametersType::ValueType	m_ParametersConvergenceTolerance
CostFunctionType::MeasureType	m_FunctionConvergenceTolerance
bool	m_AutomaticInitialSimplex
ParametersType	m_InitialSimplexDelta
bool	m_OptimizeWithRestarts
vnl_amoeba *	m_VnlOptimizer
std::ostringstream	m_StopConditionDescription
virtual void	SetMaximumNumberOfIterations (NumberOfIterationsType _arg)
virtual NumberOfIterationsType	GetMaximumNumberOfIterations () const
virtual void	SetAutomaticInitialSimplex (bool _arg)
virtual void	AutomaticInitialSimplexOn ()
virtual void	AutomaticInitialSimplexOff ()
virtual bool	GetAutomaticInitialSimplex () const
virtual void	SetOptimizeWithRestarts (bool _arg)
virtual void	OptimizeWithRestartsOn ()
virtual void	OptimizeWithRestartsOff ()
virtual bool	GetOptimizeWithRestarts () const
void	SetInitialSimplexDelta (ParametersType initialSimplexDelta, bool automaticInitialSimplex=false)
virtual ParametersType	GetInitialSimplexDelta () const
virtual void	SetParametersConvergenceTolerance (double _arg)
virtual double	GetParametersConvergenceTolerance () const
virtual void	SetFunctionConvergenceTolerance (double _arg)
virtual double	GetFunctionConvergenceTolerance () const
const std::string	GetStopConditionDescription () const override
MeasureType	GetValue () const
vnl_amoeba *	GetOptimizer () const
	AmoebaOptimizer ()
	~AmoebaOptimizer () override
void	PrintSelf (std::ostream &os, Indent indent) const override
void	ValidateSettings ()

Additional Inherited Members
Protected Types inherited from itk::SingleValuedNonLinearVnlOptimizer
using	CostFunctionAdaptorType = SingleValuedVnlCostFunctionAdaptor
Protected Member Functions inherited from itk::SingleValuedNonLinearVnlOptimizer
	SingleValuedNonLinearVnlOptimizer ()
	~SingleValuedNonLinearVnlOptimizer () override
void	SetCostFunctionAdaptor (CostFunctionAdaptorType *adaptor)
const CostFunctionAdaptorType *	GetCostFunctionAdaptor () const
CostFunctionAdaptorType *	GetCostFunctionAdaptor ()
CostFunctionAdaptorType *	GetNonConstCostFunctionAdaptor () const
void	PrintSelf (std::ostream &os, Indent indent) const override
Protected Member Functions inherited from itk::SingleValuedNonLinearOptimizer
	SingleValuedNonLinearOptimizer ()
	~SingleValuedNonLinearOptimizer () override=default
Protected Member Functions inherited from itk::NonLinearOptimizer
	NonLinearOptimizer ()=default
	~NonLinearOptimizer () override
Protected Member Functions inherited from itk::Optimizer
	Optimizer ()
	~Optimizer () override=default
virtual void	SetCurrentPosition (const ParametersType &param)
Protected Member Functions inherited from itk::Object
	Object ()
	~Object () override
bool	PrintObservers (std::ostream &os, Indent indent) const
virtual void	SetTimeStamp (const TimeStamp &timeStamp)
Protected Member Functions inherited from itk::LightObject
virtual LightObject::Pointer	InternalClone () const
	LightObject ()
virtual void	PrintHeader (std::ostream &os, Indent indent) const
virtual void	PrintTrailer (std::ostream &os, Indent indent) const
virtual	~LightObject ()
Protected Attributes inherited from itk::SingleValuedNonLinearOptimizer
CostFunctionPointer	m_CostFunction
Protected Attributes inherited from itk::Optimizer
bool	m_ScalesInitialized { false }
ParametersType	m_CurrentPosition
Protected Attributes inherited from itk::LightObject
std::atomic< int >	m_ReferenceCount

Detailed Description

Wrap of the vnl_amoeba algorithm.

AmoebaOptimizer is a wrapper around the vnl_amoeba algorithm which is an implementation of the Nelder-Meade downhill simplex problem. For most problems, it is a few times slower than a Levenberg-Marquardt algorithm but does not require derivatives of its cost function. It works by creating a simplex (n+1 points in ND space). The cost function is evaluated at each corner of the simplex. The simplex is then modified (by reflecting a corner about the opposite edge, by shrinking the entire simplex, by contracting one edge of the simplex, or by expanding the simplex) in searching for the minimum of the cost function.

The methods AutomaticInitialSimplex() and SetInitialSimplexDelta() control whether the optimizer defines the initial simplex automatically (by constructing a very small simplex around the initial position) or uses a user supplied simplex size.

The method SetOptimizeWithRestarts() indicates that the amoeabe algorithm should be rerun after if converges. This heuristic increases the chances of escaping from a local optimum. Each time the simplex is initialized with the best solution obtained by the previous runs. The edge length is half of that from the previous iteration. The heuristic is terminated if the total number of iterations is greater-equal than the maximal number of iterations (SetMaximumNumberOfIterations) or the difference between the current function value and the best function value is less than a threshold (SetFunctionConvergenceTolerance) and max(|best_parameters_i - current_parameters_i|) is less than a threshold (SetParametersConvergenceTolerance).

ITK Sphinx Examples:

• All ITK Sphinx Examples

• Amoeba Optimization

Examples

Examples/RegistrationITKv4/ImageRegistration10.cxx, Examples/RegistrationITKv4/ImageRegistration16.cxx, Examples/RegistrationITKv4/ImageRegistration17.cxx, Examples/RegistrationITKv4/ImageRegistration19.cxx, and SphinxExamples/src/Numerics/Optimizers/AmoebaOptimizer/Code.cxx.

Definition at line 66 of file itkAmoebaOptimizer.h.

Member Typedef Documentation

ConstPointer

using itk::AmoebaOptimizer::ConstPointer = SmartPointer<const Self>

Definition at line 75 of file itkAmoebaOptimizer.h.

CostFunctionAdaptorType

using itk::AmoebaOptimizer::CostFunctionAdaptorType = Superclass::CostFunctionAdaptorType

protected

Set/Get the maximum number of iterations. The optimization algorithm will terminate after the maximum number of iterations has been reached. The default value is defined as DEFAULT_MAXIMAL_NUMBER_OF_ITERATIONS.

Definition at line 171 of file itkAmoebaOptimizer.h.

InternalParametersType

using itk::AmoebaOptimizer::InternalParametersType = vnl_vector<double>

InternalParameters type alias.

Definition at line 89 of file itkAmoebaOptimizer.h.

NumberOfIterationsType

using itk::AmoebaOptimizer::NumberOfIterationsType = unsigned int

Definition at line 76 of file itkAmoebaOptimizer.h.

ParametersType

using itk::AmoebaOptimizer::ParametersType = Superclass::ParametersType

Parameters type. It defines a position in the optimization search space.

Definition at line 86 of file itkAmoebaOptimizer.h.

Pointer

using itk::AmoebaOptimizer::Pointer = SmartPointer<Self>

Definition at line 74 of file itkAmoebaOptimizer.h.

Self

using itk::AmoebaOptimizer::Self = AmoebaOptimizer

Standard "Self" type alias.

Definition at line 72 of file itkAmoebaOptimizer.h.

Superclass

using itk::AmoebaOptimizer::Superclass = SingleValuedNonLinearVnlOptimizer

Definition at line 73 of file itkAmoebaOptimizer.h.

Constructor & Destructor Documentation

AmoebaOptimizer()

itk::AmoebaOptimizer::AmoebaOptimizer ( )

protected

Set/Get the maximum number of iterations. The optimization algorithm will terminate after the maximum number of iterations has been reached. The default value is defined as DEFAULT_MAXIMAL_NUMBER_OF_ITERATIONS.

~AmoebaOptimizer()

itk::AmoebaOptimizer::~AmoebaOptimizer ( )

overrideprotected

Set/Get the maximum number of iterations. The optimization algorithm will terminate after the maximum number of iterations has been reached. The default value is defined as DEFAULT_MAXIMAL_NUMBER_OF_ITERATIONS.

Member Function Documentation

AutomaticInitialSimplexOff()

virtual void itk::AmoebaOptimizer::AutomaticInitialSimplexOff ( )

virtual

Set/Get the maximum number of iterations. The optimization algorithm will terminate after the maximum number of iterations has been reached. The default value is defined as DEFAULT_MAXIMAL_NUMBER_OF_ITERATIONS.

AutomaticInitialSimplexOn()

virtual void itk::AmoebaOptimizer::AutomaticInitialSimplexOn ( )

virtual

Set/Get the maximum number of iterations. The optimization algorithm will terminate after the maximum number of iterations has been reached. The default value is defined as DEFAULT_MAXIMAL_NUMBER_OF_ITERATIONS.

CreateAnother()

virtual::itk::LightObject::Pointer itk::AmoebaOptimizer::CreateAnother ( ) const

virtual

Create an object from an instance, potentially deferring to a factory. This method allows you to create an instance of an object that is exactly the same type as the referring object. This is useful in cases where an object has been cast back to a base class.

Reimplemented from itk::Object.

GetAutomaticInitialSimplex()

virtual bool itk::AmoebaOptimizer::GetAutomaticInitialSimplex ( ) const

virtual

Set/Get the maximum number of iterations. The optimization algorithm will terminate after the maximum number of iterations has been reached. The default value is defined as DEFAULT_MAXIMAL_NUMBER_OF_ITERATIONS.

GetFunctionConvergenceTolerance()

virtual double itk::AmoebaOptimizer::GetFunctionConvergenceTolerance ( ) const

virtual

Set/Get the maximum number of iterations. The optimization algorithm will terminate after the maximum number of iterations has been reached. The default value is defined as DEFAULT_MAXIMAL_NUMBER_OF_ITERATIONS.

GetInitialSimplexDelta()

virtual ParametersType itk::AmoebaOptimizer::GetInitialSimplexDelta ( ) const

virtual

Set/Get the maximum number of iterations. The optimization algorithm will terminate after the maximum number of iterations has been reached. The default value is defined as DEFAULT_MAXIMAL_NUMBER_OF_ITERATIONS.

GetMaximumNumberOfIterations()

virtual NumberOfIterationsType itk::AmoebaOptimizer::GetMaximumNumberOfIterations ( ) const

virtual

Set/Get the maximum number of iterations. The optimization algorithm will terminate after the maximum number of iterations has been reached. The default value is defined as DEFAULT_MAXIMAL_NUMBER_OF_ITERATIONS.

GetNameOfClass()

virtual const char* itk::AmoebaOptimizer::GetNameOfClass ( ) const

virtual

Run-time type information (and related methods).

Reimplemented from itk::SingleValuedNonLinearVnlOptimizer.

GetOptimizer()

vnl_amoeba* itk::AmoebaOptimizer::GetOptimizer

(

)

const

Method for getting access to the internal optimizer.

GetOptimizeWithRestarts()

virtual bool itk::AmoebaOptimizer::GetOptimizeWithRestarts ( ) const

virtual

Set/Get the maximum number of iterations. The optimization algorithm will terminate after the maximum number of iterations has been reached. The default value is defined as DEFAULT_MAXIMAL_NUMBER_OF_ITERATIONS.

GetParametersConvergenceTolerance()

virtual double itk::AmoebaOptimizer::GetParametersConvergenceTolerance ( ) const

virtual

Set/Get the maximum number of iterations. The optimization algorithm will terminate after the maximum number of iterations has been reached. The default value is defined as DEFAULT_MAXIMAL_NUMBER_OF_ITERATIONS.

GetStopConditionDescription()

const std::string itk::AmoebaOptimizer::GetStopConditionDescription ( ) const

overridevirtual

Report the reason for stopping.

Reimplemented from itk::Optimizer.

GetValue()

MeasureType itk::AmoebaOptimizer::GetValue

(

)

const

Return Current Value

New()

static Pointer itk::AmoebaOptimizer::New ( )

static

Method for creation through the object factory.

OptimizeWithRestartsOff()

virtual void itk::AmoebaOptimizer::OptimizeWithRestartsOff ( )

virtual

Set/Get the maximum number of iterations. The optimization algorithm will terminate after the maximum number of iterations has been reached. The default value is defined as DEFAULT_MAXIMAL_NUMBER_OF_ITERATIONS.

OptimizeWithRestartsOn()

virtual void itk::AmoebaOptimizer::OptimizeWithRestartsOn ( )

virtual

Set/Get the maximum number of iterations. The optimization algorithm will terminate after the maximum number of iterations has been reached. The default value is defined as DEFAULT_MAXIMAL_NUMBER_OF_ITERATIONS.

PrintSelf()

void itk::AmoebaOptimizer::PrintSelf ( std::ostream &  os, Indent  indent  ) const

overrideprotectedvirtual

Set/Get the maximum number of iterations. The optimization algorithm will terminate after the maximum number of iterations has been reached. The default value is defined as DEFAULT_MAXIMAL_NUMBER_OF_ITERATIONS.

Reimplemented from itk::Object.

SetAutomaticInitialSimplex()

virtual void itk::AmoebaOptimizer::SetAutomaticInitialSimplex ( bool  _arg )

virtual

Set/Get the mode which determines how the amoeba algorithm defines the initial simplex. Default is AutomaticInitialSimplexOn. If AutomaticInitialSimplex is on, the initial simplex is created with a default size. If AutomaticInitialSimplex is off, then InitialSimplexDelta will be used to define the initial simplex, setting the ith corner of the simplex as [x0[0], x0[1], ..., x0[i]+InitialSimplexDelta[i], ..., x0[d-1]].

SetCostFunction()

void itk::AmoebaOptimizer::SetCostFunction ( SingleValuedCostFunction *  costFunction )

overridevirtual

Plug in a Cost Function into the optimizer

Implements itk::SingleValuedNonLinearVnlOptimizer.

SetFunctionConvergenceTolerance()

virtual void itk::AmoebaOptimizer::SetFunctionConvergenceTolerance ( double  _arg )

virtual

The optimization algorithm will terminate when the simplex diameter and the difference in cost function values at the corners of the simplex falls below user specified thresholds. The cost function convergence threshold is set via SetFunctionConvergenceTolerance().

SetInitialSimplexDelta()

void itk::AmoebaOptimizer::SetInitialSimplexDelta	(	ParametersType	initialSimplexDelta,
		bool	automaticInitialSimplex = false
	)

Set/Get the deltas that are used to define the initial simplex when AutomaticInitialSimplex is off.

SetMaximumNumberOfIterations()

virtual void itk::AmoebaOptimizer::SetMaximumNumberOfIterations ( NumberOfIterationsType  _arg )

virtual

Set/Get the maximum number of iterations. The optimization algorithm will terminate after the maximum number of iterations has been reached. The default value is defined as DEFAULT_MAXIMAL_NUMBER_OF_ITERATIONS.

SetOptimizeWithRestarts()

virtual void itk::AmoebaOptimizer::SetOptimizeWithRestarts ( bool  _arg )

virtual

Set/Get the mode that determines if we want to use multiple runs of the Amoeba optimizer. If true, then the optimizer is rerun after it converges. The additional runs are performed using a simplex initialized with the best solution obtained by the previous runs. The edge length is half of that from the previous iteration.

SetParametersConvergenceTolerance()

virtual void itk::AmoebaOptimizer::SetParametersConvergenceTolerance ( double  _arg )

virtual

The optimization algorithm will terminate when the simplex diameter and the difference in cost function values at the corners of the simplex falls below user specified thresholds. The simplex diameter threshold is set via SetParametersConvergenceTolerance().

StartOptimization()

void itk::AmoebaOptimizer::StartOptimization ( )

overridevirtual

Start optimization with an initial value.

Reimplemented from itk::Optimizer.

ValidateSettings()

void itk::AmoebaOptimizer::ValidateSettings ( )

private

Check that the settings are valid. If not throw an exception.

Member Data Documentation

m_AutomaticInitialSimplex

bool itk::AmoebaOptimizer::m_AutomaticInitialSimplex

private

Set/Get the maximum number of iterations. The optimization algorithm will terminate after the maximum number of iterations has been reached. The default value is defined as DEFAULT_MAXIMAL_NUMBER_OF_ITERATIONS.

Definition at line 181 of file itkAmoebaOptimizer.h.

m_FunctionConvergenceTolerance

CostFunctionType::MeasureType itk::AmoebaOptimizer::m_FunctionConvergenceTolerance

private

Set/Get the maximum number of iterations. The optimization algorithm will terminate after the maximum number of iterations has been reached. The default value is defined as DEFAULT_MAXIMAL_NUMBER_OF_ITERATIONS.

Definition at line 180 of file itkAmoebaOptimizer.h.

m_InitialSimplexDelta

ParametersType itk::AmoebaOptimizer::m_InitialSimplexDelta

private

Set/Get the maximum number of iterations. The optimization algorithm will terminate after the maximum number of iterations has been reached. The default value is defined as DEFAULT_MAXIMAL_NUMBER_OF_ITERATIONS.

Definition at line 182 of file itkAmoebaOptimizer.h.

m_MaximumNumberOfIterations

NumberOfIterationsType itk::AmoebaOptimizer::m_MaximumNumberOfIterations

private

Set/Get the maximum number of iterations. The optimization algorithm will terminate after the maximum number of iterations has been reached. The default value is defined as DEFAULT_MAXIMAL_NUMBER_OF_ITERATIONS.

Definition at line 178 of file itkAmoebaOptimizer.h.

m_OptimizeWithRestarts

bool itk::AmoebaOptimizer::m_OptimizeWithRestarts

private

Set/Get the maximum number of iterations. The optimization algorithm will terminate after the maximum number of iterations has been reached. The default value is defined as DEFAULT_MAXIMAL_NUMBER_OF_ITERATIONS.

Definition at line 183 of file itkAmoebaOptimizer.h.

m_ParametersConvergenceTolerance

ParametersType::ValueType itk::AmoebaOptimizer::m_ParametersConvergenceTolerance

private

Set/Get the maximum number of iterations. The optimization algorithm will terminate after the maximum number of iterations has been reached. The default value is defined as DEFAULT_MAXIMAL_NUMBER_OF_ITERATIONS.

Definition at line 179 of file itkAmoebaOptimizer.h.

m_StopConditionDescription

std::ostringstream itk::AmoebaOptimizer::m_StopConditionDescription

private

Set/Get the maximum number of iterations. The optimization algorithm will terminate after the maximum number of iterations has been reached. The default value is defined as DEFAULT_MAXIMAL_NUMBER_OF_ITERATIONS.

Definition at line 186 of file itkAmoebaOptimizer.h.

m_VnlOptimizer

vnl_amoeba* itk::AmoebaOptimizer::m_VnlOptimizer

private

Set/Get the maximum number of iterations. The optimization algorithm will terminate after the maximum number of iterations has been reached. The default value is defined as DEFAULT_MAXIMAL_NUMBER_OF_ITERATIONS.

Definition at line 184 of file itkAmoebaOptimizer.h.

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The documentation for this class was generated from the following file:

• itkAmoebaOptimizer.h